Open Science Research Excellence
%0 Journal Article
%A Yukiko Sasaki Alam and  Shahid Alam
%D 2017 
%J  International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 122, 2017
%T Study of Syntactic Errors for Deep Parsing at Machine Translation
%U http://waset.org/publications/10006612
%V 122
%X Syntactic parsing is vital for semantic treatment by many applications related to natural language processing (NLP), because form and content coincide in many cases. However, it has not yet reached the levels of reliable performance. By manually examining and analyzing individual machine translation output errors that involve syntax as well as semantics, this study attempts to discover what is required for improving syntactic and semantic parsing.

%P 478 - 485